
"""simple docstring"""



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DetrImageProcessor




class 		a_    (			unittest.TestCase						):







    def __init__(      self      :							Optional[int]  ,       __UpperCamelCase      :							Dict  ,       __UpperCamelCase      :							Any=7  ,       __UpperCamelCase      :							List[str]=3  ,       __UpperCamelCase      :							Optional[Any]=30  ,       __UpperCamelCase      :							List[str]=4_00  ,       __UpperCamelCase      :							str=True  ,       __UpperCamelCase      :							Union[str, Any]=None  ,       __UpperCamelCase      :							str=True  ,       __UpperCamelCase      :							Dict=1 / 2_55  ,       __UpperCamelCase      :							Optional[int]=True  ,       __UpperCamelCase      :							List[Any]=[0.5, 0.5, 0.5]  ,       __UpperCamelCase      :							str=[0.5, 0.5, 0.5]  ,       __UpperCamelCase      :							List[str]=True  ,       )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
        _UpperCAmelCase        = parent
        _UpperCAmelCase        = batch_size
        _UpperCAmelCase        = num_channels
        _UpperCAmelCase        = min_resolution
        _UpperCAmelCase        = max_resolution
        _UpperCAmelCase        = do_resize
        _UpperCAmelCase        = size
        _UpperCAmelCase        = do_rescale
        _UpperCAmelCase        = rescale_factor
        _UpperCAmelCase        = do_normalize
        _UpperCAmelCase        = image_mean
        _UpperCAmelCase        = image_std
        _UpperCAmelCase        = do_pad







    def   _snake_case       (      self      :							List[str]  )		->Union[str, Any]:






        '''simple docstring'''






        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_pad": self.do_pad,
        }







    def   _snake_case       (      self      :							Union[str, Any]  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Optional[Any]=False  )		->str:






        '''simple docstring'''






        if not batched:
            _UpperCAmelCase        = image_inputs[0]
            if isinstance(__UpperCamelCase  ,       Image.Image  ):
                _UpperCAmelCase     ,_UpperCAmelCase        = image.size
            else:
                _UpperCAmelCase     ,_UpperCAmelCase        = image.shape[1], image.shape[2]
            if w < h:
                _UpperCAmelCase        = int(self.size["""shortest_edge"""] * h / w  )
                _UpperCAmelCase        = self.size["""shortest_edge"""]
            elif w > h:
                _UpperCAmelCase        = self.size["""shortest_edge"""]
                _UpperCAmelCase        = int(self.size["""shortest_edge"""] * w / h  )
            else:
                _UpperCAmelCase        = self.size["""shortest_edge"""]
                _UpperCAmelCase        = self.size["""shortest_edge"""]

        else:
            _UpperCAmelCase        = []
            for image in image_inputs:
                _UpperCAmelCase     ,_UpperCAmelCase        = self.get_expected_values([image]  )
                expected_values.append((expected_height, expected_width)  )
            _UpperCAmelCase        = max(__UpperCamelCase  ,       key=lambda __UpperCamelCase  : item[0]  )[0]
            _UpperCAmelCase        = max(__UpperCamelCase  ,       key=lambda __UpperCamelCase  : item[1]  )[1]

        return expected_height, expected_width







@require_torch
@require_vision
class 		a_    (			_UpperCAmelCase  ,    unittest.TestCase						):
    a   :					List[str]			     =					DetrImageProcessor if is_vision_available() else None







    def   _snake_case       (      self      :							Tuple  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = DetrImageProcessingTester(self  )







    @property
    def   _snake_case       (      self      :							str  )		->Optional[int]:






        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()







    def   _snake_case       (      self      :							Any  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase        = self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(__UpperCamelCase  ,       """image_mean"""  )  )
        self.assertTrue(hasattr(__UpperCamelCase  ,       """image_std"""  )  )
        self.assertTrue(hasattr(__UpperCamelCase  ,       """do_normalize"""  )  )
        self.assertTrue(hasattr(__UpperCamelCase  ,       """do_rescale"""  )  )
        self.assertTrue(hasattr(__UpperCamelCase  ,       """rescale_factor"""  )  )
        self.assertTrue(hasattr(__UpperCamelCase  ,       """do_resize"""  )  )
        self.assertTrue(hasattr(__UpperCamelCase  ,       """size"""  )  )
        self.assertTrue(hasattr(__UpperCamelCase  ,       """do_pad"""  )  )







    def   _snake_case       (      self      :							Optional[Any]  )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = self.image_processing_class.from_dict(self.image_processor_dict  )
        self.assertEqual(image_processor.size  ,       {"""shortest_edge""": 18, """longest_edge""": 13_33}  )
        self.assertEqual(image_processor.do_pad  ,       __UpperCamelCase  )

        _UpperCAmelCase        = self.image_processing_class.from_dict(
            self.image_processor_dict  ,       size=42  ,       max_size=84  ,       pad_and_return_pixel_mask=__UpperCamelCase  )
        self.assertEqual(image_processor.size  ,       {"""shortest_edge""": 42, """longest_edge""": 84}  )
        self.assertEqual(image_processor.do_pad  ,       __UpperCamelCase  )







    def   _snake_case       (      self      :							Dict  )		->Any:






        '''simple docstring'''






        pass







    def   _snake_case       (      self      :							Tuple  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        _UpperCAmelCase        = prepare_image_inputs(self.image_processor_tester  ,       equal_resolution=__UpperCamelCase  )
        for image in image_inputs:
            self.assertIsInstance(__UpperCamelCase  ,       Image.Image  )

        # Test not batched input
        _UpperCAmelCase        = image_processing(image_inputs[0]  ,       return_tensors="""pt"""  ).pixel_values

        _UpperCAmelCase     ,_UpperCAmelCase        = self.image_processor_tester.get_expected_values(__UpperCamelCase  )

        self.assertEqual(
            encoded_images.shape  ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,       )

        # Test batched
        _UpperCAmelCase     ,_UpperCAmelCase        = self.image_processor_tester.get_expected_values(__UpperCamelCase  ,       batched=__UpperCamelCase  )

        _UpperCAmelCase        = image_processing(__UpperCamelCase  ,       return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,       )







    def   _snake_case       (      self      :							Union[str, Any]  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase        = self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        _UpperCAmelCase        = prepare_image_inputs(self.image_processor_tester  ,       equal_resolution=__UpperCamelCase  ,       numpify=__UpperCamelCase  )
        for image in image_inputs:
            self.assertIsInstance(__UpperCamelCase  ,       np.ndarray  )

        # Test not batched input
        _UpperCAmelCase        = image_processing(image_inputs[0]  ,       return_tensors="""pt"""  ).pixel_values

        _UpperCAmelCase     ,_UpperCAmelCase        = self.image_processor_tester.get_expected_values(__UpperCamelCase  )

        self.assertEqual(
            encoded_images.shape  ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,       )

        # Test batched
        _UpperCAmelCase        = image_processing(__UpperCamelCase  ,       return_tensors="""pt"""  ).pixel_values

        _UpperCAmelCase     ,_UpperCAmelCase        = self.image_processor_tester.get_expected_values(__UpperCamelCase  ,       batched=__UpperCamelCase  )

        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,       )







    def   _snake_case       (      self      :							Optional[Any]  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        _UpperCAmelCase        = prepare_image_inputs(self.image_processor_tester  ,       equal_resolution=__UpperCamelCase  ,       torchify=__UpperCamelCase  )
        for image in image_inputs:
            self.assertIsInstance(__UpperCamelCase  ,       torch.Tensor  )

        # Test not batched input
        _UpperCAmelCase        = image_processing(image_inputs[0]  ,       return_tensors="""pt"""  ).pixel_values

        _UpperCAmelCase     ,_UpperCAmelCase        = self.image_processor_tester.get_expected_values(__UpperCamelCase  )

        self.assertEqual(
            encoded_images.shape  ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,       )

        # Test batched
        _UpperCAmelCase        = image_processing(__UpperCamelCase  ,       return_tensors="""pt"""  ).pixel_values

        _UpperCAmelCase     ,_UpperCAmelCase        = self.image_processor_tester.get_expected_values(__UpperCamelCase  ,       batched=__UpperCamelCase  )

        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,       )







    @slow
    def   _snake_case       (      self      :							str  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
        with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""  ,       """r"""  ) as f:
            _UpperCAmelCase        = json.loads(f.read()  )

        _UpperCAmelCase        = {"""image_id""": 3_97_69, """annotations""": target}

        # encode them
        _UpperCAmelCase        = DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""  )
        _UpperCAmelCase        = image_processing(images=__UpperCamelCase  ,       annotations=__UpperCamelCase  ,       return_tensors="""pt"""  )

        # verify pixel values
        _UpperCAmelCase        = torch.Size([1, 3, 8_00, 10_66]  )
        self.assertEqual(encoding["""pixel_values"""].shape  ,       __UpperCamelCase  )

        _UpperCAmelCase        = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]  )
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]  ,       __UpperCamelCase  ,       atol=1e-4  )  )

        # verify area
        _UpperCAmelCase        = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]  )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]  ,       __UpperCamelCase  )  )
        # verify boxes
        _UpperCAmelCase        = torch.Size([6, 4]  )
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape  ,       __UpperCamelCase  )
        _UpperCAmelCase        = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]  )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]  ,       __UpperCamelCase  ,       atol=1e-3  )  )
        # verify image_id
        _UpperCAmelCase        = torch.tensor([3_97_69]  )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]  ,       __UpperCamelCase  )  )
        # verify is_crowd
        _UpperCAmelCase        = torch.tensor([0, 0, 0, 0, 0, 0]  )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]  ,       __UpperCamelCase  )  )
        # verify class_labels
        _UpperCAmelCase        = torch.tensor([75, 75, 63, 65, 17, 17]  )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]  ,       __UpperCamelCase  )  )
        # verify orig_size
        _UpperCAmelCase        = torch.tensor([4_80, 6_40]  )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]  ,       __UpperCamelCase  )  )
        # verify size
        _UpperCAmelCase        = torch.tensor([8_00, 10_66]  )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]  ,       __UpperCamelCase  )  )







    @slow
    def   _snake_case       (      self      :							str  )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
        with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""  ,       """r"""  ) as f:
            _UpperCAmelCase        = json.loads(f.read()  )

        _UpperCAmelCase        = {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

        _UpperCAmelCase        = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""  )

        # encode them
        _UpperCAmelCase        = DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""  )
        _UpperCAmelCase        = image_processing(images=__UpperCamelCase  ,       annotations=__UpperCamelCase  ,       masks_path=__UpperCamelCase  ,       return_tensors="""pt"""  )

        # verify pixel values
        _UpperCAmelCase        = torch.Size([1, 3, 8_00, 10_66]  )
        self.assertEqual(encoding["""pixel_values"""].shape  ,       __UpperCamelCase  )

        _UpperCAmelCase        = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]  )
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]  ,       __UpperCamelCase  ,       atol=1e-4  )  )

        # verify area
        _UpperCAmelCase        = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]  )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]  ,       __UpperCamelCase  )  )
        # verify boxes
        _UpperCAmelCase        = torch.Size([6, 4]  )
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape  ,       __UpperCamelCase  )
        _UpperCAmelCase        = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]  )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]  ,       __UpperCamelCase  ,       atol=1e-3  )  )
        # verify image_id
        _UpperCAmelCase        = torch.tensor([3_97_69]  )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]  ,       __UpperCamelCase  )  )
        # verify is_crowd
        _UpperCAmelCase        = torch.tensor([0, 0, 0, 0, 0, 0]  )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]  ,       __UpperCamelCase  )  )
        # verify class_labels
        _UpperCAmelCase        = torch.tensor([17, 17, 63, 75, 75, 93]  )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]  ,       __UpperCamelCase  )  )
        # verify masks
        _UpperCAmelCase        = 82_28_73
        self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()  ,       __UpperCamelCase  )
        # verify orig_size
        _UpperCAmelCase        = torch.tensor([4_80, 6_40]  )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]  ,       __UpperCamelCase  )  )
        # verify size
        _UpperCAmelCase        = torch.tensor([8_00, 10_66]  )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]  ,       __UpperCamelCase  )  )

"""simple docstring"""



import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = SwinaSRConfig()

    if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
        _UpperCAmelCase        = 4
    elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
        _UpperCAmelCase        = 4
        _UpperCAmelCase        = 4_8
        _UpperCAmelCase        = """pixelshuffle_aux"""
    elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
        _UpperCAmelCase        = [6, 6, 6, 6]
        _UpperCAmelCase        = 6_0
        _UpperCAmelCase        = [6, 6, 6, 6]
        _UpperCAmelCase        = """pixelshuffledirect"""
    elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
        _UpperCAmelCase        = 4
        _UpperCAmelCase        = """nearest+conv"""
    elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
        _UpperCAmelCase        = 1
        _UpperCAmelCase        = 1
        _UpperCAmelCase        = 1_2_6
        _UpperCAmelCase        = 7
        _UpperCAmelCase        = 255.0
        _UpperCAmelCase        = """"""

    return config


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Tuple:





    """simple docstring"""

    if "patch_embed.proj" in name and "layers" not in name:
        _UpperCAmelCase        = name.replace("""patch_embed.proj"""       ,   """embeddings.patch_embeddings.projection"""   )
    if "patch_embed.norm" in name:
        _UpperCAmelCase        = name.replace("""patch_embed.norm"""       ,   """embeddings.patch_embeddings.layernorm"""   )
    if "layers" in name:
        _UpperCAmelCase        = name.replace("""layers"""       ,   """encoder.stages"""   )
    if "residual_group.blocks" in name:
        _UpperCAmelCase        = name.replace("""residual_group.blocks"""       ,   """layers"""   )
    if "attn.proj" in name:
        _UpperCAmelCase        = name.replace("""attn.proj"""       ,   """attention.output.dense"""   )
    if "attn" in name:
        _UpperCAmelCase        = name.replace("""attn"""       ,   """attention.self"""   )
    if "norm1" in name:
        _UpperCAmelCase        = name.replace("""norm1"""       ,   """layernorm_before"""   )
    if "norm2" in name:
        _UpperCAmelCase        = name.replace("""norm2"""       ,   """layernorm_after"""   )
    if "mlp.fc1" in name:
        _UpperCAmelCase        = name.replace("""mlp.fc1"""       ,   """intermediate.dense"""   )
    if "mlp.fc2" in name:
        _UpperCAmelCase        = name.replace("""mlp.fc2"""       ,   """output.dense"""   )
    if "q_bias" in name:
        _UpperCAmelCase        = name.replace("""q_bias"""       ,   """query.bias"""   )
    if "k_bias" in name:
        _UpperCAmelCase        = name.replace("""k_bias"""       ,   """key.bias"""   )
    if "v_bias" in name:
        _UpperCAmelCase        = name.replace("""v_bias"""       ,   """value.bias"""   )
    if "cpb_mlp" in name:
        _UpperCAmelCase        = name.replace("""cpb_mlp"""       ,   """continuous_position_bias_mlp"""   )
    if "patch_embed.proj" in name:
        _UpperCAmelCase        = name.replace("""patch_embed.proj"""       ,   """patch_embed.projection"""   )

    if name == "norm.weight":
        _UpperCAmelCase        = """layernorm.weight"""
    if name == "norm.bias":
        _UpperCAmelCase        = """layernorm.bias"""

    if "conv_first" in name:
        _UpperCAmelCase        = name.replace("""conv_first"""       ,   """first_convolution"""   )

    if (
        "upsample" in name
        or "conv_before_upsample" in name
        or "conv_bicubic" in name
        or "conv_up" in name
        or "conv_hr" in name
        or "conv_last" in name
        or "aux" in name
    ):
        # heads
        if "conv_last" in name:
            _UpperCAmelCase        = name.replace("""conv_last"""       ,   """final_convolution"""   )
        if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
            if "conv_before_upsample.0" in name:
                _UpperCAmelCase        = name.replace("""conv_before_upsample.0"""       ,   """conv_before_upsample"""   )
            if "upsample.0" in name:
                _UpperCAmelCase        = name.replace("""upsample.0"""       ,   """upsample.convolution_0"""   )
            if "upsample.2" in name:
                _UpperCAmelCase        = name.replace("""upsample.2"""       ,   """upsample.convolution_1"""   )
            _UpperCAmelCase        = """upsample.""" + name
        elif config.upsampler == "pixelshuffledirect":
            _UpperCAmelCase        = name.replace("""upsample.0.weight"""       ,   """upsample.conv.weight"""   )
            _UpperCAmelCase        = name.replace("""upsample.0.bias"""       ,   """upsample.conv.bias"""   )
        else:
            pass
    else:
        _UpperCAmelCase        = """swin2sr.""" + name

    return name


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        _UpperCAmelCase        = orig_state_dict.pop(_A   )

        if "qkv" in key:
            _UpperCAmelCase        = key.split("""."""   )
            _UpperCAmelCase        = int(key_split[1]   )
            _UpperCAmelCase        = int(key_split[4]   )
            _UpperCAmelCase        = config.embed_dim

            if "weight" in key:
                _UpperCAmelCase        = val[:dim, :]
                _UpperCAmelCase        = val[dim : dim * 2, :]
                _UpperCAmelCase        = val[-dim:, :]
            else:
                _UpperCAmelCase        = val[:dim]
                _UpperCAmelCase        = val[dim : dim * 2]
                _UpperCAmelCase        = val[-dim:]
            pass
        else:
            _UpperCAmelCase        = val

    return orig_state_dict


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = get_config(_A   )
    _UpperCAmelCase        = SwinaSRForImageSuperResolution(_A   )
    model.eval()

    _UpperCAmelCase        = torch.hub.load_state_dict_from_url(_A       ,   map_location="""cpu"""   )
    _UpperCAmelCase        = convert_state_dict(_A       ,   _A   )
    _UpperCAmelCase     ,_UpperCAmelCase        = model.load_state_dict(_A       ,   strict=_A   )

    if len(_A   ) > 0:
        raise ValueError("""Missing keys when converting: {}""".format(_A   )   )
    for key in unexpected_keys:
        if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
            raise ValueError(F"""Unexpected key {key} in state_dict"""   )

    # verify values
    _UpperCAmelCase        = """https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
    _UpperCAmelCase        = Image.open(requests.get(_A       ,   stream=_A   ).raw   ).convert("""RGB"""   )
    _UpperCAmelCase        = SwinaSRImageProcessor()
    # pixel_values = processor(image, return_tensors="pt").pixel_values

    _UpperCAmelCase        = 1_2_6 if """Jpeg""" in checkpoint_url else 2_5_6
    _UpperCAmelCase        = Compose(
        [
            Resize((image_size, image_size)   ),
            ToTensor(),
            Normalize(mean=[0.485, 0.456, 0.406]       ,   std=[0.229, 0.224, 0.225]   ),
        ]   )
    _UpperCAmelCase        = transforms(_A   ).unsqueeze(0   )

    if config.num_channels == 1:
        _UpperCAmelCase        = pixel_values[:, 0, :, :].unsqueeze(1   )

    _UpperCAmelCase        = model(_A   )

    # assert values
    if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 5_1_2, 5_1_2]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]]   )
    elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]]   )
    elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
        # TODO values didn't match exactly here
        _UpperCAmelCase        = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]]   )
    elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 5_1_2, 5_1_2]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]]   )
    elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]]   )

    assert (
        outputs.reconstruction.shape == expected_shape
    ), F"""Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"""
    assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]       ,   _A       ,   atol=1e-3   )
    print("""Looks ok!"""   )

    _UpperCAmelCase        = {
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""": (
            """swin2SR-classical-sr-x2-64"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth""": (
            """swin2SR-classical-sr-x4-64"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth""": (
            """swin2SR-compressed-sr-x4-48"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth""": (
            """swin2SR-lightweight-x2-64"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth""": (
            """swin2SR-realworld-sr-x4-64-bsrgan-psnr"""
        ),
    }
    _UpperCAmelCase        = url_to_name[checkpoint_url]

    if pytorch_dump_folder_path is not None:
        print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""   )
        model.save_pretrained(_A   )
        print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
        processor.save_pretrained(_A   )

    if push_to_hub:
        model.push_to_hub(F"""caidas/{model_name}"""   )
        processor.push_to_hub(F"""caidas/{model_name}"""   )


if __name__ == "__main__":
 a       :						Optional[int]       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
     type=str,
     help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )
 parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')

 a       :						str       		=		parser.parse_args()
 convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)